Automated modeling systems implement automated modeling algorithms that are trained using large volumes of training data. Automated modeling algorithms can use modeling techniques such as logistic regression, neural networks, support vector machines, etc. The training data for training automated modeling algorithms can be generated by or otherwise indicate certain electronic transactions or circumstances. In a training process, these training data are analyzed by one or more computing devices of an automated modeling system. The training data are grouped into attributes that are provided as inputs to the automated modeling system. The automated modeling system can use this analysis to learn from and make predictions regarding similar electronic transactions or circumstances. For example, the automated modeling system uses the attributes to learn how to generate predictive outputs involving transactions or other circumstances similar to the attributes from the training data.
The accuracy with which an automated modeling algorithm learns to make predictions of future actions can depend on the data attributes used to train the automated modeling algorithm. For instance, larger amounts of training data allow the automated modeling algorithm to identify different scenarios that may affect a predictive output, to increase the confidence that a trend associated with the training data has been properly identified, or both. Thus, if an automated modeling algorithm uses, as inputs, a larger number of attributes having some predictive relationship with a predictive output, the accuracy of the predictive output increases.
However, certain constraints may reduce the number of attributes available to a given automated modeling algorithm. In one example, an automated modeling algorithm may be implemented on a mainframe or other computing system that prevents or hinders modifications to the programming that implements the automated modeling algorithm. Such a constraint may limit the types of attributes that may be provided to the automated modeling algorithm. In another example, computing systems may be constrained in the types of training data that may be provided to an automated modeling algorithm. An example of such a constraint is a monotonicity constraint, in which the training data for a given attribute must exhibit a monotonic relationship with the predictive output. Examples of a monotonic relationship between an attribute and a predictive output include a relationship in which a value of the predictive output increases as the value of the attribute increases or a relationship in which the value of the predictive output decreases as the value of the attribute increases.
These constraints on an automated modeling algorithm may cause certain attributes to be excluded from consideration when selecting attributes for training the modeling algorithm. Excluding these attributes may decrease the accuracy or effectiveness of the trained automated modeling algorithm.